“These maps has been realized using Gargantext<\/a> from a PubMed Corpora with query “covid-19 OR coronavirus” on April 29 2020 (7.2k documents from Jan 2020 to April 28 2020). The methodology is described here<\/a><\/p>\n When you click on a term, the most related terms are displayed in a tag cloud on the right along with the Publications from PubMed that mention the most the selected terms.<\/p>\n In the Conditional map<\/b> (the first map to be loaded), links between terms represent the conditionnal probability of having one terms knowing the other in a paper (its the confidence measure). It capture the interaction of terms within the document. This map depicts well the different communities working on covid-19 with their own research angle : biological mechanisms, diagnostic, treatments, social consequences, exposure of the elderly, etc. Node size in this maps reflect the degree of the node.<\/p>\n In the Distributional maps <\/b>links between terms capture a proximity measure that takes into account the profiles of interaction of each term with the others. It can infer relevant relations between terms even if their haven’t co-occur in a paper (second order proximity measure). Node size in this graph is a fonction of the pagerank of the term in the graph.<\/p>\n![\"\"](\"https:\/\/wp.unil.ch\/citadyne-news\/files\/2020\/05\/gargantext_covid.png\")
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